1.4k citations · 1.7k across the 9 of their papers we have counts for
10 papers
DreamPaint: Few-Shot Inpainting of E-Commerce Items for Virtual Try-On without 3D Modeling
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2
We introduce DreamPaint, a framework to intelligently inpaint any e-commerce product on any user-provided context image. The context image can be, for example, the user's own image…
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI
Amirhossein Tavanaei
Understanding intermediate layers of a deep learning model and discovering the driving features of stimuli have attracted much interest, recently. Explainable artificial intelligen…
Deep Learning in Spiking Neural Networks
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh +2
In recent years, deep learning has been a revolution in the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural ne…
BP-STDP: Approximating Backpropagation using Spike Timing Dependent Plasticity
Amirhossein Tavanaei, Anthony S. Maida
The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has s…
Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals
Amirhossein Tavanaei, Anthony Maida
Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised…
Representation Learning using Event-based STDP
Amirhossein Tavanaei, Timothee Masquelier, Anthony Maida
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a ch…
Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning
Amirhossein Tavanaei, Anthony S. Maida
Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is sti…
Training a Hidden Markov Model with a Bayesian Spiking Neural Network
Amirhossein Tavanaei, Anthony S Maida
It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. Th…
Acquisition of Visual Features Through Probabilistic Spike-Timing-Dependent Plasticity
Amirhossein Tavanaei, Timothee Masquelier, Anthony S Maida
The final version of this paper has been published in IEEEXplore available at http://ieeexplore.ieee.org/document/7727213. Please cite this paper as: Amirhossein Tavanaei, Timothee…
A Spiking Network that Learns to Extract Spike Signatures from Speech Signals
Amirhossein Tavanaei, Anthony S Maida
Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks. Speech recognition, as an application involving audio coding and dynami…